Instructions to use jhoppanne/SkinCancerClassifier_Plain-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jhoppanne/SkinCancerClassifier_Plain-V1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jhoppanne/SkinCancerClassifier_Plain-V1") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("jhoppanne/SkinCancerClassifier_Plain-V1") model = AutoModelForImageClassification.from_pretrained("jhoppanne/SkinCancerClassifier_Plain-V1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from jhoppanne/SkinCancerClassifier_Plain-V1: direct link, hf CLI and curl.
- Browser
- Download file 5.18 kB
-
https://huggingface.co/jhoppanne/SkinCancerClassifier_Plain-V1/resolve/main/training_args.bin
- Command line
-
hf download hf://jhoppanne/SkinCancerClassifier_Plain-V1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/jhoppanne/SkinCancerClassifier_Plain-V1/resolve/main/training_args.bin
5.18 kB
- Xet hash:
- c30d76de222abed3a2d4dc5308d9026a80f7359f28fb9729a6a143e544317b11
- Size of remote file:
- 5.18 kB
- SHA256:
- a43d2c85e2467e2ba03a93f36f7bc5bdae1da1b72049c8129174faa4377995a8
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